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Machine learning presentation

Transcript: B2P machine learning The majority of AI experts think AI will be able to accomplish any intellectual task humans can perform by 2050. what is machine learning? WHAT definition Machine learning is a field of computer science that gives computer systems the ability to "learn" with data, without being explicitly programmed. what is the Difference between Ai,machine learning and deep learning ? we are familiar with AI(Artificial Intelligence) but recently we are hearing about "Machine Learning" and "Deep Learning" sometime used interchangeably with AI. As a result their difference become unclear. Artificial intelligence(ai) ai First coined in 1956 by John McCarthy, AI involves machines that can perform tasks that are characteristic of human intelligence like understanding languages,recognizing object and sound,learning,problem solving etc. There are two categories of AI: General AI: It consist of all characteristics of human intelligence. Narrow AI: It exhibits some facets of human intelligence and is extremely good at it. Artificial intelligence machine learning Machine Learning is a way of achieving AI. “The ability to learn without being explicitly programmed” is Machine Learning. We can get AI without using machine learning, but this would require building millions of lines of codes with complex rules and decision-trees. machine learning Deep Learning Deep learning is one of many approaches to machine learning. Other approach includes decision tree learning,Support vector Machine(SVM), Random Forest etc. Deep learning was inspired by the structure and function of the brain, namely the interconnecting of many neurons. Artificial Neural Networks (ANNs) are algorithms that mimic the biological structure of the brain. deep learning deep learning architecture comparison how machine learning work? Working of machine learning depends on its type. Types supervised learning supervised learning supervised Learning contain input variables and output variables. In supervised learning we uses machine learning algorithms to learn the mapping function from input to output variables. There are two types of problems in supervised learning: Classification Problem - Try to learn the the category of the class. Regression Problem - Try to learn the real value of the class. Classification Classifications Regressions Regressions unsupervised learning unsupervised learning In unsupervised Learning there is no right or wrong answer.we have input data points . we try to learn information about the data points. There two types of Problems : Clustering - Try to learn group in which data points belong to. Association - Try to learn the rule which better explain the data points. Clustering Clustering Result of Clustering Solution Association Association reinforcement learning reinforcement learning Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. The machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions Algorithm for each types Algkorithms for each type Different machine learning algorithms In machine learning there's something called "No free Lunch" theorem.In a nutshell,it means no one algorithm works for every problem. algo Find here commonly used machine learning algorthms commonly used machine learning algorthms Linear Regression Logistic Regression Decision Tree SVM Naive Bayes kNN K-Means Random Forest Dimensionality Reduction Algorithms Gradient Boosting algorithms GBM XGBoost LightGBM CatBoost Linear Regression price = room * 100 110 210 310 410 510 610 710 skills required skills Machine Learning Engineer The Data Science Hierarchy of Needs The Data Science Hierarchy of Needs Data Analysis Job Role Data Analysis Machine Learning Life cycle Data Scintest Machine Learning Engineer Skills Skills ML Tools DL Tools Big Data Data Visualization Cloud Technologies Data Science Lifecycle Data Scintest Jobs in Machine Learning job Egypt Salary Saudi Arabia World JOBS Specialization Degress application of machine learning Ml based products and projects ml products Benefit of Machine Learning ml products THANK YOU!! THANK YOU - Osama Mo

Machine Learning Presentation

Transcript: Brenden Schumacher Public Speaking 23 April 2020 Machine Learning Reality TODAY, AI LOOKS MORE LIKE THIS Machine Learning Reinforcement TODAY WHERE ARE WE TODAY? THE GOOD THE BAD STILL LEARNING FUTURE ON THE VERGE IN HEALTHCARE AT HOME GROWTH

Machine learning presentation

Transcript: machine learning Student: Kairgali Zhanar what is machine learning? WHAT definition Machine learning is a field of computer science that gives computer systems the ability to "learn" with data, without being explicitly programmed. types what are the types of machine learning? types of machine learning find here Supervised Learning Unsupervised Learning Reinforcement learning Machine Learning in accounting and Auditing Фml&acc find here Machine learning, gave rise to different ways of accomplishing tasks, which also affected the accountancy and auditing professions. Despite the drawbacks, implementing machine learning tools has many advantages: Increased efficiency Effectiveness by faster data analysis High-quality audit Error reduction Early identification of risks Creating a competitive advantage THANK YOU THANK YOU!!

Machine Learning Presentation

Transcript: machine learning what is machine learning? WHAT definition Machine learning is a branch of computer science, primarily artificial intelligence, which focuses on the use of data and algorithms to learn the way that humans recieve information. AI vs. Machine Learning What is Artificial intelligence(ai) ai First used in 1956 by John McCarthy, AI involves machines that can perform tasks the same way a human can like understanding languages, recognizing object and sound, learning, problem solving etc. Artificial intelligence machine learning Machine Learning is another way of using A.I. Being able to learn without straight programming is Machine Learning in a nutshell. We can use AI without using machine learning, but this would require building millions of lines of codes with complex rules and decision-trees. machine learning machine learning hierarchy ml types Supervised, Unsupervised, reinforcement learning Supervised Supervised Mainly used by orginizations to solve real world problems, supervised learning is defined by its use of labled datasets to train other algorithims to classify information or predict outcomes. Supervised learning uses traingsets that include inputs and outputs, allowing it to learn over time. unSupervised The way Unsupervised Learning works is unSupervised Supervised Mainly used for orginizations to solve real world problems, supervised learning is defined by its use of labled datasets to train other algorithims to classify information or predict outcomes. Reinforcement how machine learning work? how supervised learning supervised learning Supervised Learning uses input and output variables. Using supervised learning, machine learning algorithms learn the difference from input to output variables and their functions. unsupervised learning unsupervised learning Unsupervised Learning is when there are only data points, and the whole goal of Machine Learning is to learn about those data points. Meaning there is no right or wrong for learning. reinforcement learning reinforcement learning Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. The machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions Different machine learning algorithms algo In machine learning there's something called "No free Lunch" theorem.In a nutshell,it means no one algorithm works for every problem. Find here commonly used machine learning algorthms Linear Regression Logistic Regression Decision Tree SVM Naive Bayes kNN K-Means Random Forest Dimensionality Reduction Algorithms Gradient Boosting algorithms GBM XGBoost LightGBM CatBoost WHY Why machine learning is important ? find here Machine learning has enhanced our ability to perform almost every intellect task by replacing or significantly augmenting older approaches. This includes: Control tasks Vision tasks Language tasks Machine learning creates a world of machines that can work tirelessly, innovate, and improve themselves. It reduces both Time and cost by provide automation. Automation trend Intelligent business processes Digital assistants and bots Video surveillance Email spam and malware filtering Product recommendation Online Fraud detection Movie recommendation on Netflix Search Engine result refining Smart cars application of machine learning Ml based products and projects ml products skills required in machine learning Understanding the fundamentals of statistics, optimization, and building quantitative models and Understanding how models and data analysis actually apply to products and businesses. Knowing programming proficiency and understand the basics of system design. Working with large data sets Fundamental understanding and technical proficiency in atleast one area of this field. skills THANK YOU!! THANK YOU - Ankit Raj

Machine learning presentation

Transcript: An introduction to machine learning what is machine learning? WHAT ML is a branch of AI and computer science that focus on data usage and algorithms to imitate human learning, gradually improving its accuracy definition what is the Difference between Ai,machine learning and deep learning ? Artificial intelligence(ai) DiFFErence Artificial intelligence(ai) ai machine learning machine learning Deep Learning Deep learning is Subfield of ML and one of many approaches to machine learning. Deep learning was inspired by the structure and function of the brain, namely the interconnecting of many neurons. Artificial Neural Networks (ANNs) are algorithms that mimic the biological structure of the brain. Effective for complex, high-dimensional data deep learning deep learning architecture When when do we use machine learning? Human expertise does not exist (navigating on Mars) Humans can’t explain their expertise (speech recognition) Models must be customized (personalized medicine) Models are based on huge amounts of data (genomics) Find here types of machine learning? types types of machine learning Overview Supervised learning Supervised learning Classification Problem Try to learn the the category of the class. Regression Problem Try to learn the real value of the class. Unsupervised learning Unsupervised learning Clustering Try to learn group in which data points belong to. Association Try to learn the rule which better explain the data points. Reinforcement learning Reinforcment learning Reinforcement Learning is about learning the optimal behavior to obtain maximum reward. Discovery process is akin to Trial-and-Error. Immediate and delayed rewards are measured in the quality of actions WHY Why machine learning is important ? ML makes data-driven recommendations and decisions Machines work tirelessly, innovate, and improve themselves. It reduces both Time and cost by providing automation. Predictive Analytics find here Reasons application of machine learning ml products

Machine Learning presentation

Transcript: Machine Learning Tejveer, Faizan, Fahim, Amanjot What is Machine Learning? What is Machine Learning Machine learning is the process of algorithms that can self improve automatically through experience and using data. How Machine Learning Works How machine learning works Decision Process Error Function Model Optimization Process Supervised machine learning Unsupervised machine learning Semi-supervised learning Machine learning methods History of Machine Learning 1950 - Turing Test 1952 - Arthur Samuel's Game 1957 - Frank Rosenblatt's perceptron 1967 - Nearest Neighbor 1979 - Stanford's Cart 1985 - NetTalk 1997 - IBM's Deep Blue 1999 - University of Chicago creates cancer computer 2006 - Geoffrey Hinton coins term Deep Learning (Lead to present day) History Movies and Machine Learning Movies and Machine Learning - Throughout history, tech seen on movies helped shape today's - Metropolis by Fritz Lang - 2001 by Kubrick - Blade Runner Why is it Important in the Present? Why is it important in the present Importance of collecting data for future Various uses worldwide in different systems System Examples Speech Recognition Car self driving feature Facial Recognition General Medicine Many more examples! Machine Learning in 10 Years Where Will This Technology be in 10 Years? - Machine learning will be implemented almost anywhere - Pharmacies will implement machine learning - Prediction of diseases, discovery of drugs, and electronic health records - Car companies are already implementing machine learning in self driving cars Quantum Computing How Quantum Computing will Effect the Future of Computer Learning What is quantum computing? How will it effect machine learning? How much more better is quantum computing? How will machine learning effect the economy in the future? Economy - Market predicted to rise from 8.43 billion to around 117.19 billion - Will be implemented in self driving cars and the pharmaceutical market - Car & pharmaceutical market will increase which will cause economy to increase

Machine Learning Presentation

Transcript: My presentation MACHINE LEARNING by : Vinod Reddy B Thank You! Simple Introduction and History of MACHINE LEARNING INTRODUCTION IS AN ART OF COMPUTER THINKING Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. History The term machine learning was coined in 1959 by Arthur Samuel Arthur Samuel is an American IBMer and pioneer in the field of computer gaming and artificial intelligence. Conventional Programming : The approach of conventional programming is to feed the computer with a set of instructions for a defined set of scenarios. ex : C, C++, Java, JavaScript, Python, etc DIFFERENCE Difference b/w Conventional Programming v/s Machine Learning Difference Types Supervised Learning Example :Weighting Machine Unsupervised Learning Example : Cricket Scores Reinforcement Learning Real Time Examples for ML Examples or Applications Traffic prediction Virtual personal assistant Online transportation Social media services Email spam filtering Product recommendation Online fraud detection Example with Pictures Advantages of Machine Learning Merits and Demerits Fast, Accurate, Efficient. Automation of most applications. Wide range of real life applications. Enhanced cyber security and spam detection. No human Intervention is needed. Handling multi-dimensional data. Disadvantages of Machine Learning Disadvantages It is very difficult to identify and rectify the errors. Data Acquisition. Interpretation of results Requires more time and space. Conclusion Conclusion We have a simple overview of some techniques in machine learning. Furthermore, there are more and more techniques apply machine learning as a solution. In the future, machine learning will play an important role in our daily life.

Machine learning presentation

Transcript: Supervised Machine learning The majority of AI experts think AI will be able to accomplish any intellectual task humans can perform by 2050. what is machine learning? WHAT definition Machine learning is a field of computer science that gives computer systems the ability to "learn" with data, without being explicitly programmed. what is the Difference between Ai,machine learning and deep learning ? we are familiar with AI(Artificial Intelligence) but recently we are hearing about "Machine Learning" and "Deep Learning" sometime used interchangeably with AI. As a result their difference become unclear. Artificial intelligence(ai) ai First coined in 1956 by John McCarthy, AI involves machines that can perform tasks that are characteristic of human intelligence like understanding languages,recognizing object and sound,learning,problem solving etc. There are two categories of AI: General AI: It consist of all characteristics of human intelligence. Narrow AI: It exhibits some facets of human intelligence and is extremely good at it. machine learning “The ability to learn without being explicitly programmed” is Machine Learning. machine learning Deep Learning Deep learning is one of many approaches to machine learning. Deep learning was inspired by the structure and function of the brain. deep learning types what are the types of machine learning? types of machine learning Types Supervised Learning Unsupervised Learning Reinforcement learning how machine learning work? how Working of machine learning depends on its type. reinforcement learning reinforcement learning Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. The machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions unsupervised learning unsupervised learning In unsupervised Learning there is no right or wrong answer.we have input data points . we try to learn information about the data points. There two types of Problems : Clustering - Try to learn group in which data points belong to. Association - Try to learn the rule which better explain the data points. supervised learning supervised learning supervised Learning contain input variables and output variables. In supervised learning we uses machine learning algorithms to learn the mapping function from input to output variables. There are two types of problems in supervised learning: Classification Problem - Try to learn the the category of the class. Regression Problem - Try to learn the real value of the class. Supervised learning Today's topic Supervised Learning is the process of making an algorithm to learn to map an input to a particular output. This is achieved using the labelled datasets that we have collected. What? In supervised learning, our goal is to learn the mapping function (f), which refers to being able to understand how the input (X) should be matched with output (Y) using available data how to train? pros and cons Examples EXAMPLES: House prices How’s the weather today? Is it a cat or a dog? Spam filtering Who are the unhappy customers? commonly used machine learning algorithms algo Nearest Neighbor Naive Bayes Decision Trees Linear Regression Support Vector Machines (SVM) Linear regression Linear regression Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Naïve Bayes Classifier Naïve Bayes Classifier Algorithm Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and mainly used in text classification that includes a high-dimensional training dataset.It is a probabilistic classifier, which means it predicts on the basis of the probability of an object. Some popular examples of Naïve Bayes Algorithm are spam filtration, Sentimental analysis, and classifying articles. DECISION Tree Decision Tree Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves. The leaves are the decisions or the final outcomes. And the decision nodes are where the data is split. application of machine learning Ml based products and projects ml products Virtual Personal Assistants Predictions while Commuting Social Media Services Email Spam and Malware Filtering Search Engine Result Refining BioInformatics Product Recommendations ANy Questions QNA THANK YOU!! THANK YOU AND PRAY FOR THE PEOPLE AFFECTED BY COVID-19

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